Abstract
In the near-synonym lexical choice task, the best alternative out of a set of near-synonyms is selected to fill a lexical gap in a text. We experiment on an approach of an extensive set, over 650, linguistic features to represent the context of a word, and a range of machine learning approaches in the lexical choice task. We extend previous work by experimenting with unsupervised and semi-supervised methods, and use automatic feature selection to cope with the problems arising from the rich feature set. It is natural to think that linguistic analysis of the word context would yield almost perfect performance in the task but we show that too many features, even linguistic, introduce noise and make the task difficult for unsupervised and semi-supervised methods. We also show that purely syntactic features play the biggest role in the performance, but also certain semantic and morphological features are needed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Apidianaki, M.: Data-driven semantic analysis for multilingual WSD and lexical selection in translation. In: Proceedings of EACL 2009, pp. 77–85. ACL (2009)
Arppe, A.: Univariate, bivariate, and multivariate methods in corpus-based lexicography–a study of synonymy. Ph.D. thesis, University of Helsinki, Finland (2008)
Baayen, R.H., Arppe, A.: Statistical classification and principles of human learning. In: Proceedings of QITL, vol. 4 (2011)
Carpuat, M., Wu, D.: Improving statistical machine translation using word sense disambiguation. In: Proceedings of EMNLP-CoNLL 2007, pp. 61–72 (2007)
Comon, P.: Independent component analysis, a new concept? Signal processing 36(3), 287–314 (1994)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Edmonds, P.: Choosing the word most typical in context using a lexical co-occurrence network. In: Proceedings of EACL 1997, pp. 507–509. ACL (1997)
Edmonds, P., Hirst, G.: Near-synonymy and lexical choice. Computational Linguistics 28(2), 105–144 (2002)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Haykin, S.: Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs (1994)
Inkpen, D., Graeme, H.: Building and using a lexical knowledge base of near-synonym differences. Computational Linguistics 32(2), 223–262 (2006)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, New York (2001)
Kurimo, M., Creutz, M., Turunen, V.: Overview of morpho challenge in CLEF 2007. In: Working Notes of the CLEF 2007 Workshop, pp. 19–21 (2007)
Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review 104(2), 211–240 (1997)
McCarthy, D.: Lexical substitution as a task for WSD evaluation. In: Proceedings of SIGLEX/SENSEVAL 2002, pp. 109–115. ACL (2002)
McCarthy, D., Navigli, R.: SemEval-2007 task 10: English lexical substitution task. In: Proceedings of SemEval 2007, pp. 48–53. ACL (2007)
McCullagh, P., Nelder, J.A.: Generalized Linear Models. Chapman & Hall, New York (1990)
Mihalcea, R., Sinha, R., McCarthy, D.: SemEval-2010 Task 2: Cross-lingual lexical substitution. In: Proceedings of SemEval 2010, pp. 9–14. ACL (2010)
Sahlgren, M.: The Word-Space Model. Ph.D. thesis, Department of Linguistics, Stockholm University, Stockholm, Sweden (2006)
Schütze, H.: Dimensions of meaning. In: Proceedings of SC 1992, pp. 787–796. IEEE (1992)
Tapanainen, P., Järvinen, T.: A non-projective dependency parser. In: Proceedings of Applied Natural Language Processing, pp. 64–71. ACL (1997)
Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Proceedings of ACM SIGIR 1994, pp. 61–69. Springer, Heidelberg (1994)
Wang, T., Hirst, G.: Near-synonym lexical choice in latent semantic space. In: Proceedings of Coling 2010, pp. 1182–1190. ACL (2010)
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of ACL 1995, pp. 189–196. ACL (1995)
Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paukkeri, MS., Väyrynen, J., Arppe, A. (2012). Exploring Extensive Linguistic Feature Sets in Near-Synonym Lexical Choice. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-28601-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28600-1
Online ISBN: 978-3-642-28601-8
eBook Packages: Computer ScienceComputer Science (R0)